Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging

Identification of traits and functional connectivity-based neurotraits of chronic pain

Psychological and personality factors, socioeconomic status, and brain properties all contribute to chronic pain but have essentially been studied independently. Here, we administered a broad battery of questionnaires to patients with chronic back pain (CBP) and collected repeated sessions of resting-state functional magnetic resonance imaging (fMRI) brain scans. Clustering and network analyses applied on the questionnaire data revealed four orthogonal dimensions accounting for 56% of the variance and defining chronic pain traits. Two of these traits-Pain-trait and Emote-trait-were associated with back pain characteristics and could be related to distinct distributed functional networks in a cross-validation procedure, identifying neurotraits. These neurotraits showed good reliability across four fMRI sessions acquired over five weeks. Further, traits and neurotraits all related to the income, emphasizing the importance of socioeconomic status within the personality space of chronic pain. Our approach is a first step in providing metrics aimed at unifying the psychology and the neurophysiology of chronic pain applicable across diverse clinical conditions.

Differentiating trait pain from state pain: a window into brain mechanisms underlying how we experience and cope with pain

Across various biological and psychological attributes, individuals have a set point around which they can fluctuate transiently into various states. However, if one remains in a different state other than their set point for a considerable period (eg, induced by a disease), this different state can be considered to be a new set point that also has associated surrounding states. This concept is instructive for understanding chronic pain, where an individual's set point may maladaptively shift such that they become stuck at a new set point of pain (trait pain), from which pain can fluctuate on different timescales (ie, pain states). Here, we discuss the importance of considering trait and state pains in neuroimaging studies of brain structure and function to gain an understanding of not only an individual's current pain state but also more broadly to their trait pain, which may be more reflective of their general condition.

Emotion schemas are embedded in the human visual system

Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category-related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system.

Introduction to a Special Issue on Innovations and Controversies in Brain Imaging of Pain: Methods and Interpretations

This special issue comprised 14 articles from leaders in the field, that provide opinions and reviews of concepts that are central to the next generation of pain imaging studies. Topics include cutting-edge technologies and approaches that are at the forefront of such studies, as well as developments toward biomarkers of pain and clinical applications that bring us closer to harnessing understanding of pains and its modulation to offer better options to those suffering from pain.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Dispositional negativity, cognition, and anxiety disorders: An integrative translational neuroscience framework

When extreme, anxiety can become debilitating. Anxiety disorders, which often first emerge early in development, are common and challenging to treat, yet the underlying mechanisms have only recently begun to come into focus. Here, we review new insights into the nature and biological bases of dispositional negativity, a fundamental dimension of childhood temperament and adult personality and a prominent risk factor for the development of pediatric and adult anxiety disorders. Converging lines of epidemiological, neurobiological, and mechanistic evidence suggest that dispositional negativity increases the likelihood of psychopathology via specific neurocognitive mechanisms, including attentional biases to threat and deficits in executive control. Collectively, these observations provide an integrative translational framework for understanding the development and maintenance of anxiety disorders in adults and youth and set the stage for developing improved intervention strategies.

False-positive neuroimaging: Undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding

Hypothesis testing in neuroimaging studies relies heavily on treating named anatomical regions (e.g., "the amygdala") as unitary entities. Though data collection and analyses are conducted at the voxel level, inferences are often based on anatomical regions. The discrepancy between the unit of analysis and the unit of inference leads to ambiguity and flexibility in analyses that can create a false sense of reproducibility. For example, hypothesizing effects on "amygdala activity" does not provide a falsifiable and reproducible definition of precisely which voxels or which patterns of activation should be observed. Rather, it comprises a large number of unspecified sub-hypotheses, leaving room for flexible interpretation of findings, which we refer to as "model degrees of freedom." From a survey of 135 functional Magnetic Resonance Imaging studies in which researchers claimed replications of previous findings, we found that 42.2% of the studies did not report any quantitative evidence for replication such as activation peaks. Only 14.1% of the papers used exact coordinate-based or a priori pattern-based models. Of the studies that reported peak information, 42.9% of the 'replicated' findings had peak coordinates more than 15 mm away from the 'original' findings, suggesting that different brain locations were activated, even when studies claimed to replicate prior results. To reduce the flexible and qualitative region-level tests in neuroimaging studies, we recommend adopting quantitative spatial models and tests to assess the spatial reproducibility of findings. Techniques reviewed here include permutation tests on peak distance, Bayesian MANOVA, and a priori multivariate pattern-based models. These practices will help researchers to establish precise and falsifiable spatial hypotheses, promoting a cumulative science of neuroimaging.

Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment

Pain is a subjective sensory experience that can, mostly, be reported but cannot be directly measured or quantified. Nevertheless, a suite of biomarkers related to mechanisms, neural activity, and susceptibility offer the possibility-especially when used in combination-to produce objective pain-related indicators with the specificity and sensitivity required for diagnosis and for evaluation of risk of developing pain and of analgesic efficacy. Such composite biomarkers will also provide improved understanding of pain pathophysiology.

The emotional brain: Fundamental questions and strategies for future research

Emotions play a central role in human experience. Over time, methods for manipulating emotion have become increasingly refined and techniques for making sense of the underlying neurobiology have become ever more powerful and precise, enabling new insights into the organization of emotions in the brain. Yet recent years have witnessed a remarkably vigorous debate about the nature and origins of emotion, with leading scientists raising compelling concerns about the canon of facts and principles that has inspired and guided the field for the past quarter century. Here, we consider ways in which recent neuroimaging research informs this dialogue. By focusing attention on the most important outstanding questions about the nature of emotion and the architecture of the emotional brain, we hope to stimulate the kinds of work that will be required to move the field forward. Addressing these questions is critical, not just for understanding the mind, but also for elucidating the root causes of many of its disorders.

The Rehabilitation Treatment Specification System: Implications for Improvements in Research Design, Reporting, Replication, and Synthesis

Despite significant advances in measuring the outcomes of rehabilitation interventions, little progress has been made in specifying the therapeutic ingredients and processes that cause measured changes in patient functioning. The general approach to better clarifying the process of treatment has been to develop reporting checklists and guidelines that increase the amount of detail reported. However, without a framework instructing researchers in how to describe their treatment protocols in a manner useful to or even interpretable by others, requests for more detail will fail to improve our understanding of the therapeutic process. In this article, we describe how the Rehabilitation Treatment Specification System (RTSS) provides a theoretical framework that can improve research intervention reporting and enable testing and refinement of a protocol's underlying treatment theories. The RTSS framework provides guidance for researchers to explicitly state their hypothesized active ingredients and targets of treatment as well as for how the individual ingredients in their doses directly affect the treatment targets. We explain how theory-based treatment specification has advantages over checklist approaches for intervention design, reporting, replication, and synthesis of evidence in rehabilitation research. A complex rehabilitation intervention is used as a concrete example of the differences between an RTSS-based specification and the Template for Intervention Description and Replication checklist. The RTSS's potential to advance the rehabilitation field can be empirically tested through efforts to use the framework with existing and newly developed treatment protocols.

A Theory-Driven System for the Specification of Rehabilitation Treatments

The field of rehabilitation remains captive to the black-box problem: our inability to characterize treatments in a systematic fashion across diagnoses, settings, and disciplines, so as to identify and disseminate the active ingredients of those treatments. In this article, we describe the Rehabilitation Treatment Specification System (RTSS), by which any treatment employed in rehabilitation may be characterized, and ultimately classified according to shared properties, via the 3 elements of treatment theory: targets, ingredients, and (hypothesized) mechanisms of action. We discuss important concepts in the RTSS such as the distinction between treatments and treatment components, which consist of 1 target and its associated ingredients; and the distinction between targets, which are the direct effects of treatment, and aims, which are downstream or distal effects. The RTSS includes 3 groups of mutually exclusive treatment components: Organ Functions, Skills and Habits, and Representations. The last of these comprises not only thoughts and feelings, but also internal representations underlying volitional action; the RTSS addresses the concept of volition (effort) as a critical element for many rehabilitation treatments. We have developed an algorithm for treatment specification which is illustrated and described in brief. The RTSS stands to benefit the field in numerous ways by supplying a coherent, theory-based framework encompassing all rehabilitation treatments. Using a common framework, researchers will be able to test systematically the effects of specific ingredients on specific targets; and their work will be more readily replicated and translated into clinical practice.

Advancing Rehabilitation Practice Through Improved Specification of Interventions

Rehabilitation clinicians strive to provide cost-effective, patient-centered care that optimizes outcomes. A barrier to this ideal is the lack of a universal system for describing, or specifying, rehabilitation interventions. Current methods of description vary across disciplines and settings, creating barriers to collaboration, and tend to focus mostly on functional deficits and anticipated outcomes, obscuring connections between clinician behaviors and changes in functioning. The Rehabilitation Treatment Specification System (RTSS) is the result of more than a decade of effort by a multidisciplinary group of rehabilitation clinicians and researchers to develop a theory-based framework to specify rehabilitation interventions. The RTSS describes interventions for treatment components, which consist of a target (functional change brought about as a direct result of treatment), ingredients (actions taken by clinicians to change the target), and a hypothesized mechanism of action, as stated in a treatment theory. The RTSS makes explicit the connections between functional change and clinician behavior, and recognizes the role of patient effort in treatment implementation. In so doing, the RTSS supports clinicians' efforts to work with their patients to set achievable goals, select appropriate treatments, adjust treatment plans as needed, encourage patient participation in the treatment process, communicate with team members, and translate research findings to clinical care. The RTSS may help both expert and novice clinicians articulate their clinical reasoning processes in ways that benefit treatment planning and clinical education, and may improve the design of clinical documentation systems, leading to more effective justification and reimbursement for services. Interested clinicians are invited to apply the RTSS in their local settings.

Lost in translation

Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level-a cardinal requirement for any intervention-their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved.

Lost in translation

Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level-a cardinal requirement for any intervention-their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved.